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增加baseline评估makefile命令

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RYDE-WORK 2026-03-02 11:12:54 +08:00
parent ead579b25c
commit 447f2543f7
4 changed files with 5 additions and 143 deletions
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5
Makefile
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@ -80,6 +80,11 @@ benchmark: requirements
$(PYTHON_INTERPRETER) -m lnp_ml.modeling.benchmark main $(MPNN_FLAG) $(DEVICE_FLAG)
$(PYTHON_INTERPRETER) -m lnp_ml.modeling.benchmark test $(DEVICE_FLAG)
## Evaluate baseline method on public test splits (test.csv vs preds.csv in cv_*)
.PHONY: baseline
baseline: requirements
$(PYTHON_INTERPRETER) scripts/evaluate_external.py
#################################################################################
# TRAINING (Nested CV + Optuna) #
#################################################################################

29
lnp_ml/features.py
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@ -1,29 +0,0 @@
from pathlib import Path
from loguru import logger
from tqdm import tqdm
import typer
from lnp_ml.config import PROCESSED_DATA_DIR
app = typer.Typer()
@app.command()
def main(
# ---- REPLACE DEFAULT PATHS AS APPROPRIATE ----
input_path: Path = PROCESSED_DATA_DIR / "dataset.csv",
output_path: Path = PROCESSED_DATA_DIR / "features.csv",
# -----------------------------------------
):
# ---- REPLACE THIS WITH YOUR OWN CODE ----
logger.info("Generating features from dataset...")
for i in tqdm(range(10), total=10):
if i == 5:
logger.info("Something happened for iteration 5.")
logger.success("Features generation complete.")
# -----------------------------------------
if __name__ == "__main__":
app()

29
lnp_ml/plots.py
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@ -1,29 +0,0 @@
from pathlib import Path
from loguru import logger
from tqdm import tqdm
import typer
from lnp_ml.config import FIGURES_DIR, PROCESSED_DATA_DIR
app = typer.Typer()
@app.command()
def main(
# ---- REPLACE DEFAULT PATHS AS APPROPRIATE ----
input_path: Path = PROCESSED_DATA_DIR / "dataset.csv",
output_path: Path = FIGURES_DIR / "plot.png",
# -----------------------------------------
):
# ---- REPLACE THIS WITH YOUR OWN CODE ----
logger.info("Generating plot from data...")
for i in tqdm(range(10), total=10):
if i == 5:
logger.info("Something happened for iteration 5.")
logger.success("Plot generation complete.")
# -----------------------------------------
if __name__ == "__main__":
app()

85
scripts/data_cleaning.py
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@ -1,85 +0,0 @@
"""数据清洗脚本：修正原始数据中的问题"""
from pathlib import Path
import numpy as np
import pandas as pd
import typer
from loguru import logger
from lnp_ml.config import RAW_DATA_DIR, INTERIM_DATA_DIR
app = typer.Typer()
@app.command()
def main(
input_path: Path = RAW_DATA_DIR / "internal_deleted_uncorrected.xlsx",
output_path: Path = INTERIM_DATA_DIR / "internal_corrected.csv",
):
"""
清洗原始数据，修正已知问题。
修正内容：
1. 修正肌肉注射组 Biodistribution_muscle=0.7745 的数据
2. 修复阳性对照组 (Amine="Crtl") 的数据
3. 按给药途径分组进行 z-score 标准化
4. 对 size 列取 log
"""
logger.info(f"Loading data from {input_path}")
df = pd.read_excel(input_path, header=2)
logger.info(f"Loaded {len(df)} samples")
# 修正肌肉注射组 0.7745 的数据
logger.info("Correcting Biodistribution_muscle=0.7745 rows...")
rows_to_correct = df[df["Biodistribution_muscle"] == 0.7745]
for index, row in rows_to_correct.iterrows():
total_biodistribution = pd.to_numeric(row[[
"Biodistribution_lymph_nodes",
"Biodistribution_heart",
"Biodistribution_liver",
"Biodistribution_spleen",
"Biodistribution_lung",
"Biodistribution_kidney",
"Biodistribution_muscle"
]]).sum()
df.at[index, "Biodistribution_lymph_nodes"] = pd.to_numeric(row["Biodistribution_lymph_nodes"]) / total_biodistribution
df.at[index, "Biodistribution_heart"] = pd.to_numeric(row["Biodistribution_heart"]) / total_biodistribution
df.at[index, "Biodistribution_liver"] = pd.to_numeric(row["Biodistribution_liver"]) / total_biodistribution
df.at[index, "Biodistribution_spleen"] = pd.to_numeric(row["Biodistribution_spleen"]) / total_biodistribution
df.at[index, "Biodistribution_lung"] = pd.to_numeric(row["Biodistribution_lung"]) / total_biodistribution
df.at[index, "Biodistribution_kidney"] = pd.to_numeric(row["Biodistribution_kidney"]) / total_biodistribution
df.at[index, "Biodistribution_muscle"] = pd.to_numeric(row["Biodistribution_muscle"]) / total_biodistribution
df.at[index, "quantified_total_luminescence"] = pd.to_numeric(row["quantified_total_luminescence"]) / (1 - 0.7745)
df.at[index, "unnormalized_delivery"] = df.at[index, "quantified_total_luminescence"]
logger.info(f" Corrected {len(rows_to_correct)} rows")
# 修复阳性对照组的数据
logger.info("Fixing control group (Amine='Crtl')...")
rows_to_override = df["Amine"] == "Crtl"
df.loc[rows_to_override, "quantified_total_luminescence"] = 1
df.loc[rows_to_override, "unnormalized_delivery"] = 1
logger.info(f" Fixed {rows_to_override.sum()} rows")
# 分别对肌肉注射组和静脉注射组重新进行 z-score 标准化
logger.info("Z-score normalizing delivery by Route_of_administration...")
df["unnormalized_delivery"] = pd.to_numeric(df["unnormalized_delivery"], errors="coerce")
df["quantified_delivery"] = (
df.groupby("Route_of_administration")["unnormalized_delivery"]
.transform(lambda x: (x - x.mean()) / x.std())
)
# 对 size 列取 log
logger.info("Log-transforming size column...")
df["size"] = pd.to_numeric(df["size"], errors="coerce")
df["size"] = np.log(df["size"].replace(0, np.nan)) # 避免 log(0)
# 保存
output_path.parent.mkdir(parents=True, exist_ok=True)
df.to_csv(output_path, index=False)
logger.success(f"Saved cleaned data to {output_path}")
if __name__ == "__main__":
app()